p53基因突变与骨髓增殖性肿瘤临床特征及预后关系

探究p53 基因突变与骨髓增殖性肿瘤（MPN）临床特征及预后的关系。方法 选取2017年1月～2020年1月本院收治的MPN患者126例，二代测序法检测患者p53 基因突变情况。对患者进行随访，统计患者总生存（OS）时间和累计生存率；分析p53 基因突变对患者临床特征、预后的影响。结果 126例MPN患者中12例（9.52%）检出p53基因突变，突变主要位于4~8号外显子上，不同类型患者的p53 基因突变检出率比较差异无统计学意义（P＞0.05）。p53突变组患者年龄大于p53 非突变组，WBC水平低于p53 非突变组（P＜0.05），两组患者的染色体核型、IPSS预后分层比较差异无统计学意义（P＞0.05）；p53 非突变组患者的OS时间、累计生存率均明显高于p53突变组患者（P＜0.05）。结论 MPN患者p53 基因突变发生率较高，与患者年龄、WBC水平、异常核型及IPSS预后分层相关，p53 基因突变会影响患者的预后，可作为临床筛查预后不良高风险人群的客观指标。     

Reliability and validity of the gait classification system in children with cerebral palsy (GCS-CP)

BackgroundGait classification systems (GCS) may enable clinicians to differentiate gait patterns into clinically significant categories that assist in clinical decision-making and assessment of outcomes. Davids and Bagley in 2014 [1] described a GCS for children with cerebral palsy (GCS-CP). The purpose of our study was to use the GCS-CP for the first time on a sample of patients with CP and to evaluate the reliability and utility of the classification system.MethodsThe gait of 131 children with CP was retrospectively reviewed and classified according to Davids and Bagley’s classification using two-dimensional (2D) video and three-dimensional (3D) lower limb kinematics and kinetics. Gross Motor Function Classification System (GMFCS) levels were determined, and the Gait Profile Scores (GPS) calculated to characterize the sample concerning gait classification. The comparison between the groups was performed using the Kruskal-Wallis test with respect to the non-normal distribution of the data. The intrarater and interrater reliability was determined using the Kappa index (k) statistics with 95% CI.ResultsAll GCS-CP groups were represented within the evaluated sample. Of the 131 cases evaluated, 127 (96.95%) were able to be classified with respect to sagittal plane stance phase gait deviations. All patients in the sample were able to be classified with respect to sagittal plane swing phase and transverse plane gait deviations. The interrater reliability was 0.596 and 0.485 for the first and second levels of the classification, respectively, according to the Fleiss’s Kappa statistics. Intrarater reliability was 0.776 and 0.714 for the raters one and two, respectively, according to the Cohen’s Kappa statistics.SignificanceThe GCS-CP exhibited clinical utility, successfully classifying almost all subjects with CP in two planes, based upon kinematic and kinetic data. The classification is valid and has moderate interrater and moderate to substantial intrarater reliability.     

Automatic segmentation of thoracic CT images using three deep learning models

PurposeDeep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.Materials and methodsWe used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).ResultsThe obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.ConclusionThe performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.     

Secondary Qualitative Analysis of Moral Injury in Obstetric and Neonatal Nurses

ObjectiveTo investigate the presence of symptoms of moral injury in obstetric and neonatal nurses.DesignA secondary qualitative analysis using an analytic expansion of three primary studies.SettingPostal mail and electronic surveys.ParticipantsI used three primary studies: participants in the first consisted of 78 labor and delivery nurses, participants in the second consisted of 75 nurse-midwives, and participants in the third consisted of 22 NICU nurses.MethodsI used Krippendorff’s content analysis method for qualitative data to reanalyze the three primary data sets. The categories I used in this analysis were the 10 symptoms of moral injury that are assessed by the Moral Injury Symptoms Scale–Health Professionals Version.ResultsWhen combining the three types of obstetric and neonatal participants, the top three most frequently cited symptoms of moral injury were moral concern, guilt, and self-condemnation. For participants in labor and delivery units and NICUs, moral concern was the most often described symptom, whereas for participants in midwifery it was guilt. None of the participants reported loss of meaning in their lives, loss of faith, or religious struggle. Participants who worked in NICUs did not describe any symptoms of shame or difficulty forgiving.ConclusionIn addition to the primary symptoms of moral injury, reported secondary consequences of moral injury can include depression, anxiety, anger, self-harm, and social problems. Interventions such as acceptance and commitment therapy are needed to help nurses address the potential for moral injury and repair its effects. Since the COVID-19 pandemic, now more than ever, moral injury needs to be recognized in obstetric and neonatal nurses and not just in the military population.     

社区严重精神障碍患者规律面访情况及其影响因素研究

背景严重精神障碍患者因自知力欠缺及高复发性、高致残性等特点，对社会稳定造成严重威胁。做好社区面访管理工作意义重大，通过定期面访患者本人，可以更好地综合评估患者病情，以便及时采取措施，提高管理服务质量。目的了解社区严重精神障碍患者规律面访情况及其影响因素，为提高社区患者的面访服务管理质量提供参考依据。方法2020年10—12月，利用2019年底江苏省严重精神障碍管理系统中建档管理的无锡市严重精神障碍患者基础档案及随访管理信息，分析当年度无锡市社区严重精神障碍患者规律面访情况，采用Logistic回归分析规律面访的影响因素。结果共纳入27 778例研究对象，其规律面访率为81.08%（22 523/27 778）。Logistic回归分析结果显示，非本地户籍〔OR（95%CI）=0.704（0.640，0.775）〕、年龄≤44岁〔OR（95%CI）=0.522（0.472，0.578）〕、学历为高中及以上〔高中/中专OR（95%CI）=0.493（0.446，0.545），大专及以上OR（95%CI）=0.470（0.415，0.532）〕、目前有正式工作〔OR（95%CI）=0.715（0.668，0.766）〕、未婚〔OR（95%CI）=0.746（0.665，0.838）〕、经济状况为非贫困〔OR（95%CI）=0.587（0.517，0.666）〕、没有进行抗精神病药物治疗〔OR（95%CI）=0.491（0.440，0.548）〕、服药时长0~10年〔OR（95%CI）=0.881（0.778，0.998）〕、不是"以奖代补"监护补助对象〔OR（95%CI）=0.807（0.704，0.926）〕、不参加社区康复服务〔OR（95%CI）=0.844（0.716，0.996）〕是患者规律面访的消极影响因素（P<0.05）。结论无锡市社区严重精神障碍患者规律面访率尚需进一步提高，应重点关注非本地户籍、年龄≤44岁、高中及以上学历、目前有正式工作、未婚、经济非贫困、没有进行抗精神病药物治疗、服药时长0~10年、不是监护补助对象和不参加社区康复服务的社区严重精神障碍患者。可通过制定倾斜性政策、开展宣传教育、降低患者病耻感、改善患者疾病认知、加强患者社会支持等措施提高患者规律面访率。     

Novice nurses’ experiences from teamwork in the emergency department: A qualitative content analysis

Background and aimTeamwork is assumed crucial in nursing. Nevertheless, nursing shortages in medical centers have thus far compelled nurse managers to use novice nurses. The present study aimd to explaining the challenges and experiences of novice nurses regarding teamwork in emergency departments.Materials and methodsUsing qualitative content analysis, 11 novice nurses working at emergency department, participated in this study. Data were collected with semi structured interview and analyzed by Graneheim and Lundman.ResultsThe data analysis led to the emergence of four themes of “essential teamwork skills development”, “contradictory relationships between team members”, “unpleasant feelings and experiences”, and, “personal growth and maturation during teamwork”.ConclusionFostering different personal aspects as prerequisites for teamwork, novice nurses will be able to deal with unpleasant feelings and experiences as well as contradictory relationships between team members in an effective manner. This challenging experience will thus give rise to personal growth and maturation during teamwork among them in emergency department.     

Moral Distress and Burnout in NICU Nurses

The objectives of this study were to factor analyze the Moral Distress Scale–Revised (MDS-R) in NICU nurses and to evaluate the relationships among dimensions of the MDS-R and the demoralization, exhaustion, and loss of motive dimensions of the Burnout Measure (BM). A total of 142 NICU nurses completed modified pen-and-paper versions of the MDS-R and BM. Exploratory and confirmatory factor analyses showed that the MDS-R-14 was a relatively good fit for the data. The compromised care dimension predicted BM demoralization (β = 0.24) and exhaustion (β = 0.22), the futile care dimension predicted BM exhaustion (β = 0.18), and the untruthful care dimension predicted BM demoralization (β = .25). Strategies to mitigate moral distress and resulting burnout in NICU nurses should address futile care, compromised care, and untruthful care.     

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform ( https://oncotools.elixir.no ), and can also be accessed as a stand-alone R package ( https://github.com/sigven/oncoEnrichR ).